1. Technical Field
The present invention relates to a technique for selecting optimum equalization learning parameters in a mobile communication system based on estimation of Doppler frequency of a signal.
2. Description of the Background Art
In a mobile communication system, a channel between a base station and a mobile station is corrupted due to fading as the channel gain varies over time. The channel is also corrupted due to thermal noise and noise generated by components of the receiver. In general, a signal as received over a channel must be deconvoluted to remove past values of the transmitted signal which are intermingled with instantaneous values of the transmitted signal. Typically, an equalizer is used to deconvolute the channel. The equalizer must be adaptive to learn the environment since fading is different in each channel.
In a mobile communication system, the mobile station moves with respect to the base station. As a result of the relative movement, the channel is also frequency shifted due to Doppler speed. An equalizer is typically designed to deconvolute the channel to remove the convolutional components. However, a typical equalizer cannot distinguish between the channel and the effects of Doppler speed. If the Doppler frequency is large, the equalizer will have difficulties deconvoluting the channel. For instance, the equalizer will attempt to adapt to the Doppler frequency in order to keep up with the relative movement between the base station and the mobile station, but will fail to deconvolute the channel accurately and quickly.
In the present invention, optimum equalization learning parameters are determined to make deconvolution independent of the impact of Doppler frequency. A functional relationship between the received signal and a parameter indicative of an estimate of Doppler frequency is derived. Values of estimated Doppler frequency are mapped with respect to variance between instantaneous energy of the received signal and mean energy of the received signal based on a theoretical functional relationship. The variance of a received signal is determined and the corresponding estimated Doppler frequency is thereafter derived from the map. An optimum equalization learning parameter is then selected in accordance with the estimate of Doppler frequency. Accuracy and speed of the equalization process are thus improved, independent of the impact of Doppler frequency.